Lectures related to Search Algorithms: Linear vs Logarithmic

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Provides an overview of essential search and sort algorithms in computer science.

Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.

Covers the complexity analyses of algorithms and their worst-case time complexities.

Covers the time complexity of algorithms and includes a quiz.

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Explores algorithmic challenges, time complexity, optimization, recursion, and probability calculations.

Explores dichotomy search algorithms, analyzing complexity and implementation details for efficient searching in sorted lists.

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

Explores examples of algorithm complexity, sorting, and polynomial computations.

Covers basic algorithm ingredients, search algorithms, control structures, and algorithm correctness.

Explores algorithm growth, search complexities, and logarithmic properties in depth, with practical exercises included.

Introduces selection and insertion sorting algorithms, explaining their correctness and time complexity.

Explores the Quick Sort algorithm, focusing on its divide-and-conquer approach and time complexity analysis.

Explores the Merge Sort algorithm, applying the Divide and Conquer approach to sorting arrays efficiently.

Explores linear search, insertion sort, coin change, maximum matching, and the halting problem in algorithms.

Explores the theory of computation, undecidable problems, complexity measurement, and the P complexity class.

Explains recursive sorting using Merge Sort and its linearithmic complexity.

Explores sorting and recursive algorithms, including complexity analysis, maximal value, and binary search.